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Searching for authors named "Dimitrios Makris" – sorted by Relevance.

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  • Finding Paths in Video Sequences  
  • by Dimitrios Makris, Tim Ellis — 2001
  • …This paper investigates the task of identifying frequently-used pathways from video sequences of natural outdoor scenes. Path models are adaptively learnt from the accumulation of trajectory data over many image frames. Labelled paths are used as an efficient means for compressing the trajectory dat…
  • Cited by 11 (3 self) – Add To MetaCart
  • Spatial and Probabilistic Modelling of Pedestrian Behaviour  
  • by Dimitrios Makris, Tim Ellis — 2002 — British Machine Vision Conference 2002, vol.2
  • …This paper investigates the combination of spatial and probabilistic models for reasoning about pedestrian behaviour in visual surveillance systems. Models are learnt by a multi-step unsupervised method and they are used for trajectory labelling and atypical behaviour detection.…
  • Cited by 13 (3 self) – Add To MetaCart
  • Automatic Learning of an Activity-Based Semantic Scene Model  
  • by Dimitrios Makris, Tim Ellis — 2003 — In Advanced Video and Signal Based Surveillance
  • …“©2003 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other w…
  • Cited by 17 (5 self) – Add To MetaCart
  • Path Detection in Video Surveillance  
  • by Dimitrios Makris, Tim Ellis — 2002 — Image and Vision Computing
  • …This paper addresses the problem of automatically extracting frequently used pedestrian pathways from video sequences of natural outdoor scenes. Path models are learnt from the accumulation of trajectory data over long time periods, and can be used to augment the classification of subsequent track d…
  • Cited by 16 (5 self) – Add To MetaCart
  • Finding Paths in Video Sequences  
  • by Dimitrios Makris And, Dimitrios Makris, Tim Ellis — 2001
  • …This paper investigates the task of identifying frequently-used pathways from video sequences of natural outdoor scenes. Path models are adaptively learnt from the accumulation of trajectory data over many image frames. Labelled paths are used as an efficient means for compressing the trajectory dat…
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  • Intelligent Visual Surveillance: Towards Cognitive Vision Systems  
  • by Dimitrios Makris, Tim Ellis, James Black
  • …Abstract: Automated visual surveillance systems are required to emulate the cognitive abilities of surveillance personnel, who are able to detect, recognise and assess the severity of suspicious, unusual and threatening behaviours. We describe the architecture of our surveillance system, emphasising…
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  • Integration of Local Image Cues for Probabilistic 2D Pose Recovery * 1  
  • by Paul Kuo, Dimitrios Makris, Najla Megherbi, Jean-christophe Nebel
  • …Abstract. A novel probabilistic formulation for 2-D human pose recovery from monocular images is proposed. It relies on a bottom-up approach based on an iterative process between clustering and body model fitting. Body parts are segmented from the foreground by clustering a set of images cues. Clust…
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  • Visual Learning in Surveillance Systems  
  • by Dimitrios Makris, Supervised Dr, Tim Ellis — 2001
  • …Contents Contents ..................................................................... ......................................................... 2 1. Introduction......................................................... ............................................................ 3 1.1 Motion Detec…
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  • Ellis Validation of Blind Region Learning and Tracking  
  • by James Black, Dimitrios Makris, Tim Ellis — 2005 — Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation
  • …Multi view tracking systems enable an object’s identity to be preserved as it moves through a wide area surveillance network of cameras. One limitation of these systems is an inability to track objects between blind regions, i.e. parts of the scene that are not observable by the network of cameras. …
  • Cited by 2 (1 self) – Add To MetaCart
  • Learning Scene Semantics  
  • by Dimitrios Makris, Tim Ellis, James Black — 2004 — In: ECOVISION 2004 Early Cognitive Vision Workshop. Isle of Skye
  • …Automated visual surveillance systems are required to emulate the cognitive abilities of surveillance personnel, who are able to detect, recognise and assess the severity of suspicious, unusual and threatening behaviours. We describe the architecture of our surveillance system, emphasising some of i…
  • Cited by 1 (0 self) – Add To MetaCart
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